title: ApicoAMP: the first computational model for identifying apicoplast-targeted transmembrane proteins in Apicomplexa.
abstract: BACKGROUND
  Computational identification of apicoplast-targeted proteins is important in drug target determination for diseases such as malaria. While there are established methods for identifying proteins with a bipartite signal in multiple species of Apicomplexa, not all apicoplast-targeted proteins possess this bipartite signature. The publication of recent experimental findings of apicoplast membrane proteins, called transmembrane proteins, that do not possess a bipartite signal has made it feasible to devise a machine learning approach for identifying this new class of apicoplast-targeted proteins computationally.
  
  
  RESULTS
  In this work, we develop a method for predicting apicoplast-targeted transmembrane proteins for multiple species of Apicomplexa, whereby several classifiers trained on different feature sets and based on different algorithms are evaluated and combined in an ensemble classification model to obtain the best expected performance. The feature sets considered are the hydrophobicity and composition characteristics of amino acids over transmembrane domains, the existence of short sequence motifs over cytosolically disposed regions, and Gene Ontology (GO) terms associated with given proteins. Our model, ApicoAMP, is an ensemble classification model that combines decisions of classifiers following the majority vote principle. ApicoAMP is trained on a set of proteins from 11 apicomplexan species and achieves 91% overall expected accuracy.
  
  
  CONCLUSIONS
  ApicoAMP is the first computational model capable of identifying apicoplast-targeted transmembrane proteins in Apicomplexa. The ApicoAMP prediction software is available at http://code.google.com/p/apicoamp/ and http://bcb.eecs.wsu.edu.
authors.affiliation: School of Electrical Engineering and Computer Science, Washington State University, Pullman, WA 99164, USA.
authors.forename: Gokcen
authors.initials: G
authors.lastname: Cilingir
authors.forename: Audrey O T
authors.initials: AO
authors.lastname: Lau
authors.forename: Shira L
authors.initials: SL
authors.lastname: Broschat
mesh_terms: D020816:Amino Acid Motifs; D000596:Amino Acids / Q000032:analysis / Q000235:genetics; D016782:Apicomplexa / Q000737:chemistry / Q000235:genetics*; D063546:Apicoplasts / Q000737:chemistry / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D057927:Hydrophobic and Hydrophilic Interactions; D008565:Membrane Proteins / Q000737:chemistry / Q000235:genetics*; D021381:Protein Transport; D015800:Protozoan Proteins / Q000737:chemistry / Q000235:genetics*
chemical_list: D000596:Amino Acids; D008565:Membrane Proteins; D015800:Protozoan Proteins
keywords: Apicomplexa; Apicoplast; Apicoplast-targeted membrane proteins; Ensemble classification models; Machine learning; Transmembrane proteins